Atomistic mechanisms of phase transitions from Machine Learning

Identifying and characterizing atomic mechanisms of phase changes is of fundamental importance in the study of the kinetics of the nucleation and growth process. This talk will describe a new approach in extracting information from atomistic simulations of phase transitions using Machine Learning (ML) methods. In this approach the local neighborhood of atoms is characterized in terms of symmetry functions that are used as input to a ML algorithm trained to identify atomic rearrangements leading to structural transformations. The application of the method is illustrated using Molecular Dynamics simulations of crystallization from the liquid or amorphous phase. We also discuss how meaningful physical properties can be extracted from the output of the ML algorithm.